
import pandas as pd
import numpy as np
import seaborn as sns
import seaborn as sb
import matplotlib.pyplot as plt
import statsmodels.api as sm
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from statsmodels.stats.outliers_influence import variance_inflation_factor
import math
from sklearn.preprocessing import MinMaxScaler
import warnings
warnings.filterwarnings("ignore")


df = pd.read_csv('compactiv.csv')
df.head().T


df.shape

(8192, 22)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8192 entries, 0 to 8191
Data columns (total 22 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   lread     8192 non-null   int64  
 1   lwrite    8192 non-null   int64  
 2   scall     8192 non-null   int64  
 3   sread     8192 non-null   int64  
 4   swrite    8192 non-null   int64  
 5   fork      8192 non-null   float64
 6   exec      8192 non-null   float64
 7   rchar     8088 non-null   float64
 8   wchar     8177 non-null   float64
 9   pgout     8192 non-null   float64
 10  ppgout    8192 non-null   float64
 11  pgfree    8192 non-null   float64
 12  pgscan    8192 non-null   float64
 13  atch      8192 non-null   float64
 14  pgin      8192 non-null   float64
 15  ppgin     8192 non-null   float64
 16  pflt      8192 non-null   float64
 17  vflt      8192 non-null   float64
 18  runqsz    8192 non-null   object 
 19  freemem   8192 non-null   int64  
 20  freeswap  8192 non-null   int64  
 21  usr       8192 non-null   int64  
dtypes: float64(13), int64(8), object(1)
memory usage: 1.4+ MB


df.drop_duplicates(inplace=True)


df.dtypes

lread         int64
lwrite        int64
scall         int64
sread         int64
swrite        int64
fork        float64
exec        float64
rchar       float64
wchar       float64
pgout       float64
ppgout      float64
pgfree      float64
pgscan      float64
atch        float64
pgin        float64
ppgin       float64
pflt        float64
vflt        float64
runqsz       object
freemem       int64
freeswap      int64
usr           int64
dtype: object


df.isnull().sum()

lread         0
lwrite        0
scall         0
sread         0
swrite        0
fork          0
exec          0
rchar       104
wchar        15
pgout         0
ppgout        0
pgfree        0
pgscan        0
atch          0
pgin          0
ppgin         0
pflt          0
vflt          0
runqsz        0
freemem       0
freeswap      0
usr           0
dtype: int64


df.describe().T


print("df:",df.median())

df: lread             7.0
lwrite            1.0
scall          2051.5
sread           166.0
swrite          117.0
fork              0.8
exec              1.2
rchar        125473.5
wchar         46619.0
pgout             0.0
ppgout            0.0
pgfree            0.0
pgscan            0.0
atch              0.0
pgin              2.8
ppgin             3.8
pflt             63.8
vflt            120.4
freemem         579.0
freeswap    1289289.5
usr              89.0
dtype: float64


pd.DataFrame({'value': df['rchar'], 'Missing?': df['rchar'].isnull()})


df['rchar'].fillna(df.rchar.median(), inplace = True)  
df


pd.DataFrame({'value': df['wchar'], 'Missing?': df['wchar'].isnull()})


df['wchar'].fillna(df.wchar.median(), inplace = True)  
df


df.isin([0]).sum()

lread        675
lwrite      2684
scall          0
sread          0
swrite         0
fork          21
exec          21
rchar          0
wchar          0
pgout       4878
ppgout      4878
pgfree      4869
pgscan      6448
atch        4575
pgin        1220
ppgin       1220
pflt           3
vflt           0
runqsz         0
freemem        0
freeswap       0
usr          283
dtype: int64


nums = []
cats = []
for i in df.columns:
    if df[i].dtype !='O':
        nums.append(i)
    else:
        cats.append(i)
print(nums)
print(cats)

['lread', 'lwrite', 'scall', 'sread', 'swrite', 'fork', 'exec', 'rchar', 'wchar', 'pgout', 'ppgout', 'pgfree', 'pgscan', 'atch', 'pgin', 'ppgin', 'pflt', 'vflt', 'freemem', 'freeswap', 'usr']
['runqsz']


for x in nums:
    plt.figure(figsize=(5,10))
    sns.boxplot(df[x])
    plt.show()


def remove_outlier(column):
    sorted(column)
    q1=df[column].quantile(0.25)
    q3=df[column].quantile(0.75)
    iqr=q3-q1
    lower=q1-1.5*iqr
    upper=q3+1.5*iqr
    return lower,upper


for i in nums[:-1]:
    lower,upper=remove_outlier(i)
    df[i]=np.where(df[i]>upper,upper,df[i])
    df[i]=np.where(df[i]<lower,lower,df[i])


for i in nums:
    sns.boxplot(df[i],showmeans=True)
    plt.show()


scaler = MinMaxScaler()
columns=['lread','lwrite','scall','sread','swrite','fork','exec','rchar','wchar','pgout','ppgout','pgscan','pgfree','atch','pgin','ppgin','pflt','vflt','freemem','freeswap','usr']
 
df_scaled = scaler.fit_transform(df[columns].to_numpy())
df_scaled = pd.DataFrame(df_scaled,columns=columns)
print("Scaled Dataset Using MinMaxScaler")
df_scaled['runqsz']=df['runqsz']
df_scaled.head().T

Scaled Dataset Using MinMaxScaler


df_scaled.runqsz.unique()

array(['CPU_Bound', 'Not_CPU_Bound'], dtype=object)


df= pd.get_dummies(df, prefix='CPU_Bound', columns=['runqsz'])


df.head()


sns.distplot(df['lread'])

<AxesSubplot:xlabel='lread', ylabel='Density'>


sns.distplot(df['lwrite'])

<AxesSubplot:xlabel='lwrite', ylabel='Density'>


sns.distplot(df['scall'])

<AxesSubplot:xlabel='scall', ylabel='Density'>


sns.distplot(df['sread'])

<AxesSubplot:xlabel='sread', ylabel='Density'>


sns.distplot(df['swrite'])

<AxesSubplot:xlabel='swrite', ylabel='Density'>


sns.distplot(df['fork'])

<AxesSubplot:xlabel='fork', ylabel='Density'>


sns.distplot(df['exec'])

<AxesSubplot:xlabel='exec', ylabel='Density'>


sns.distplot(df['rchar'])

<AxesSubplot:xlabel='rchar', ylabel='Density'>


sns.distplot(df['wchar'])

<AxesSubplot:xlabel='wchar', ylabel='Density'>


sns.distplot(df['pgout'])

<AxesSubplot:xlabel='pgout', ylabel='Density'>


sns.distplot(df['ppgout'])

<AxesSubplot:xlabel='ppgout', ylabel='Density'>


sns.distplot(df['pgfree'])

<AxesSubplot:xlabel='pgfree', ylabel='Density'>


sns.distplot(df['pgscan'])

<AxesSubplot:xlabel='pgscan', ylabel='Density'>


sns.distplot(df['atch'])

<AxesSubplot:xlabel='atch', ylabel='Density'>


sns.distplot(df['pgin'])

<AxesSubplot:xlabel='pgin', ylabel='Density'>


sns.distplot(df['ppgin'])

<AxesSubplot:xlabel='ppgin', ylabel='Density'>


sns.distplot(df['pflt'])

<AxesSubplot:xlabel='pflt', ylabel='Density'>


sns.distplot(df['freemem'])

<AxesSubplot:xlabel='freemem', ylabel='Density'>


sns.distplot(df['freeswap'])

<AxesSubplot:xlabel='freeswap', ylabel='Density'>


sns.distplot(df['usr'])

<AxesSubplot:xlabel='usr', ylabel='Density'>


sns.pairplot(df_scaled, diag_kind='kde',size = 3)
plt.show()


plt.figure(figsize=(20,20))
sns.heatmap(df.corr(), annot=True)

<AxesSubplot:>


plt.figure(figsize=(20,20))
sns.heatmap(df.cov(), annot=True)

<AxesSubplot:>


df.corr()


df.cov()


vif_data = pd.DataFrame()
vif_data["feature"] = X.columns
  
# calculating VIF for each feature
vif_data["VIF"] = [variance_inflation_factor(X.values, i)
                          for i in range(len(X.columns))]
cols_to_remove=list(vif_data.loc[vif_data['VIF']>=5]['feature'])
print(cols_to_remove)
  
vif_data.sort_values('VIF', ascending=False)
print(vif_data)

['freeswap']
    feature       VIF
0    lwrite  1.540820
1     scall  4.326486
2      exec  2.517951
3     rchar  3.100179
4     wchar  2.899042
5    pgscan       NaN
6      atch  1.892260
7   freemem  3.167247
8  freeswap  5.744688


new_df=X.drop(['lread', 'sread', 'swrite', 'fork', 'pgout', 'ppgout', 'pgfree', 'pgin', 'ppgin', 'pflt', 'vflt', 'CPU_Bound_CPU_Bound', 'CPU_Bound_Not_CPU_Bound'], axis=1)
new_df


X = df.drop('usr', axis=1)
y = df[['usr']]


X.head()


vif = [variance_inflation_factor(X.values, ix) for ix in range(X.shape[1])]


i=0
for column in X.columns:
    if i < 15:
        print (column ,"--->",  vif[i])
        i = i+1

lread ---> 5.232534882470838
lwrite ---> 4.260737491684919
scall ---> 2.9890877339622937
sread ---> 6.517014292159949
swrite ---> 5.639797517443181
fork ---> 12.947438478657318
exec ---> 3.1375249704246078
rchar ---> 2.087445737239059
wchar ---> 1.6056667145387191
pgout ---> 11.474543882811679
ppgout ---> 30.685097541787272
pgfree ---> 17.17512180642015
pgscan ---> nan
atch ---> 1.8596581052295806
pgin ---> 13.733882306297474


X = new_df
y = df[['usr']]


from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25 , random_state=1)


model.predict(X_train)

5745     68.397776
1930     53.316204
5622     60.481046
5340    102.002734
2255     98.826406
           ...    
7935     74.811305
5192     96.452824
3980     69.561082
235      70.027195
5157     94.588283
Length: 6144, dtype: float64


regression_model = LinearRegression()
regression_model.fit(X_train, y_train)

LinearRegression()


# Let us explore the coefficients for each of the independent attributes

for idx, col_name in enumerate(X_train.columns):
    print("The coefficient for {} is {}".format(col_name, regression_model.coef_[0][idx]))

The coefficient for lwrite is -0.058127152795354
The coefficient for scall is 4.839980419857865e-06
The coefficient for exec is -1.6805010105580211
The coefficient for rchar is -1.6342812718241054e-05
The coefficient for wchar is -1.0627935572899192e-05
The coefficient for pgscan is -9.785922072680364e-14
The coefficient for atch is 0.9660984921841413
The coefficient for freemem is -0.0021454803784539084
The coefficient for freeswap is 3.096245172144866e-05


# Let us check the intercept for the model
intercept = regression_model.intercept_[0]

print("The intercept for our model is {}".format(intercept))

The intercept for our model is 52.703408616461346


# R square on training data
regression_model.score(X_train, y_train)

0.5513661898690928


#R square on testing data
regression_model.score(X_test, y_test)

0.5447312405803018


#RMSE on training data
predicted_train=regression_model.fit(X_train, y_train).predict(X_train)
np.sqrt(mean_squared_error(y_train,predicted_train))

12.16989876561295


##RMSE on testing data
predicted_test=regression_model.fit(X_train, y_train).predict(X_test)
np.sqrt(mean_squared_error(y_test,predicted_test))

12.870554763335232


# concatenate X and y into a single dataframe
data_train = pd.concat([X_train, y_train], axis=1)
data_test=pd.concat([X_test,y_test],axis=1)
data_train.head()


data_train.columns

Index(['lwrite', 'scall', 'exec', 'rchar', 'wchar', 'pgscan', 'atch',
       'freemem', 'freeswap', 'usr'],
      dtype='object')


expr= 'usr ~ lwrite+scall+exec+rchar+wchar+pgscan+atch+freemem+freeswap'


import statsmodels.formula.api as smf
lm1 = smf.ols(formula= expr, data = data_train).fit()
lm1.params

Intercept    5.270341e+01
lwrite      -5.812715e-02
scall        4.839980e-06
exec        -1.680501e+00
rchar       -1.634281e-05
wchar       -1.062794e-05
pgscan       2.952750e-15
atch         9.660985e-01
freemem     -2.145480e-03
freeswap     3.096245e-05
dtype: float64


print(lm1.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                    usr   R-squared:                       0.551
Model:                            OLS   Adj. R-squared:                  0.551
Method:                 Least Squares   F-statistic:                     942.5
Date:                Fri, 29 Jul 2022   Prob (F-statistic):               0.00
Time:                        20:24:50   Log-Likelihood:                -24072.
No. Observations:                6144   AIC:                         4.816e+04
Df Residuals:                    6135   BIC:                         4.822e+04
Df Model:                           8                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------
Intercept     52.7034      0.740     71.224      0.000      51.253      54.154
lwrite        -0.0581      0.017     -3.387      0.001      -0.092      -0.024
scall        4.84e-06      0.000      0.039      0.969      -0.000       0.000
exec          -1.6805      0.089    -18.977      0.000      -1.854      -1.507
rchar      -1.634e-05    1.1e-06    -14.837      0.000   -1.85e-05   -1.42e-05
wchar      -1.063e-05   2.55e-06     -4.163      0.000   -1.56e-05   -5.62e-06
pgscan      2.953e-15   8.48e-16      3.482      0.001    1.29e-15    4.62e-15
atch           0.9661      0.322      3.002      0.003       0.335       1.597
freemem       -0.0021      0.000    -16.465      0.000      -0.002      -0.002
freeswap    3.096e-05   4.82e-07     64.284      0.000       3e-05    3.19e-05
==============================================================================
Omnibus:                     1872.393   Durbin-Watson:                   2.055
Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6395.147
Skew:                          -1.524   Prob(JB):                         0.00
Kurtosis:                       6.961   Cond. No.                     1.46e+23
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 5.74e-31. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.


# Calculate MSE
mse = np.mean((lm1.predict(data_train.drop('usr',axis=1))-data_train['usr'])**2)


#Root Mean Squared Error - RMSE
rmse=np.sqrt(mse)
rmse

12.169898765612954


np.sqrt(lm1.mse_resid)

12.17882207031835


100 - (rmse/y_train.mean())*100

usr    85.523778
dtype: float64


# Prediction on Test data
y_pred = lm1.predict(data_test)


y_pred = regression_model.predict(X_test)


sns.set(rc = {'figure.figsize':(7,7)})
ax=sns.regplot(x=y_test,y=y_pred,ci=None,color ='blue');
ax.set_xlabel('Actual')
ax.set_ylabel('Predicted')

Text(0, 0.5, 'Predicted')


for i,j in np.array(lm1.params.reset_index()):
    print('({}) * {} +'.format(round(j,2),i),end=' ')

(52.7) * Intercept + (-0.06) * lwrite + (0.0) * scall + (-1.68) * exec + (-0.0) * rchar + (-0.0) * wchar + (0.0) * pgscan + (0.97) * atch + (-0.0) * freemem + (0.0) * freeswap +

	count	mean	std	min	25%	50%	75%	max
lread	8192.0	1.955969e+01	53.353799	0.0	2.0	7.0	20.000	1845.00
lwrite	8192.0	1.310620e+01	29.891726	0.0	0.0	1.0	10.000	575.00
scall	8192.0	2.306318e+03	1633.617322	109.0	1012.0	2051.5	3317.250	12493.00
sread	8192.0	2.104800e+02	198.980146	6.0	86.0	166.0	279.000	5318.00
swrite	8192.0	1.500582e+02	160.478980	7.0	63.0	117.0	185.000	5456.00
fork	8192.0	1.884554e+00	2.479493	0.0	0.4	0.8	2.200	20.12
exec	8192.0	2.791998e+00	5.212456	0.0	0.2	1.2	2.800	59.56
rchar	8088.0	1.973857e+05	239837.493526	278.0	34091.5	125473.5	267828.750	2526649.00
wchar	8177.0	9.590299e+04	140841.707911	1498.0	22916.0	46619.0	106101.000	1801623.00
pgout	8192.0	2.285317e+00	5.307038	0.0	0.0	0.0	2.400	81.44
ppgout	8192.0	5.977229e+00	15.214590	0.0	0.0	0.0	4.200	184.20
pgfree	8192.0	1.191971e+01	32.363520	0.0	0.0	0.0	5.000	523.00
pgscan	8192.0	2.152685e+01	71.141340	0.0	0.0	0.0	0.000	1237.00
atch	8192.0	1.127505e+00	5.708347	0.0	0.0	0.0	0.600	211.58
pgin	8192.0	8.277960e+00	13.874978	0.0	0.6	2.8	9.765	141.20
ppgin	8192.0	1.238859e+01	22.281318	0.0	0.6	3.8	13.800	292.61
pflt	8192.0	1.097938e+02	114.419221	0.0	25.0	63.8	159.600	899.80
vflt	8192.0	1.853158e+02	191.000603	0.2	45.4	120.4	251.800	1365.00
freemem	8192.0	1.763456e+03	2482.104511	55.0	231.0	579.0	2002.250	12027.00
freeswap	8192.0	1.328126e+06	422019.426957	2.0	1042623.5	1289289.5	1730379.500	2243187.00
usr	8192.0	8.396887e+01	18.401905	0.0	81.0	89.0	94.000	99.00

	0	1	2	3	4
lread	0.021277	0.0	0.319149	0.0	0.106383
lwrite	0.0	0.0	0.12	0.0	0.04
scall	0.305725	0.009151	0.307975	0.007651	0.033153
sread	0.129778	0.021333	0.272	0.010667	0.058667
swrite	0.168975	0.038781	0.310249	0.024931	0.085873
fork	0.040816	0.040816	0.408163	0.040816	0.081633
exec	0.029851	0.029851	0.358209	0.029851	0.059701
rchar	0.066119	0.000278	0.20493	0.20493	0.20493
wchar	0.229117	0.030057	0.132904	0.031301	0.046642
pgout	0.0	0.0	0.0	0.0	0.0
ppgout	0.0	0.0	0.0	0.0	0.0
pgscan	0.0	0.0	0.0	0.0	0.0
pgfree	0.0	0.0	0.0	0.0	0.0
atch	0.0	0.0	0.8	0.0	0.0
pgin	0.068049	0.0	0.255183	0.008506	0.042531
ppgin	0.077381	0.0	0.279762	0.005952	0.035714
pflt	0.04426	0.043237	0.415491	0.043154	0.104564
vflt	0.046686	0.029633	0.392017	0.029579	0.084462
freemem	1.0	1.0	0.140526	1.0	0.12554
freeswap	0.770522	0.832369	0.45258	0.829996	0.783651
usr	0.959596	0.979798	0.878788	0.989899	0.909091
runqsz	CPU_Bound	Not_CPU_Bound	Not_CPU_Bound	Not_CPU_Bound	Not_CPU_Bound

	lread	lwrite	scall	sread	swrite	fork	exec	rchar	wchar	pgout	...	atch	pgin	ppgin	pflt	vflt	freemem	freeswap	usr	CPU_Bound_CPU_Bound	CPU_Bound_Not_CPU_Bound
lread	1.000000	0.834674	0.333572	0.326032	0.306542	0.365676	0.364062	0.255544	0.174410	0.208258	...	0.230628	0.283252	0.290564	0.375250	0.421079	-0.201369	-0.243903	-0.292817	0.136419	-0.136419
lwrite	0.834674	1.000000	0.140213	0.148028	0.132097	0.093166	0.121778	0.117504	0.132595	0.090726	...	0.126769	0.113368	0.118388	0.102631	0.135243	-0.099558	-0.149630	-0.148599	0.053363	-0.053363
scall	0.333572	0.140213	1.000000	0.763001	0.742206	0.474491	0.440010	0.386875	0.331933	0.297487	...	0.306359	0.335380	0.325487	0.485361	0.548081	-0.388969	-0.357864	-0.325174	0.247893	-0.247893
sread	0.326032	0.148028	0.763001	1.000000	0.876652	0.528047	0.369723	0.576127	0.415059	0.301658	...	0.292044	0.346654	0.344586	0.529364	0.597892	-0.349887	-0.369897	-0.388342	0.228820	-0.228820
swrite	0.306542	0.132097	0.742206	0.876652	1.000000	0.519148	0.313323	0.419759	0.430126	0.274708	...	0.264283	0.307420	0.302481	0.505560	0.563163	-0.350318	-0.336869	-0.350115	0.216083	-0.216083
fork	0.365676	0.093166	0.474491	0.528047	0.519148	1.000000	0.774268	0.370995	0.122990	0.197047	...	0.201768	0.247715	0.236216	0.939125	0.932579	-0.136045	-0.133135	-0.351232	0.122392	-0.122392
exec	0.364062	0.121778	0.440010	0.369723	0.313323	0.774268	1.000000	0.324990	0.124145	0.230712	...	0.252416	0.299591	0.288017	0.758255	0.763429	-0.191919	-0.184268	-0.325499	0.125470	-0.125470
rchar	0.255544	0.117504	0.386875	0.576127	0.419759	0.370995	0.324990	1.000000	0.486317	0.250881	...	0.265281	0.369470	0.390376	0.381489	0.438870	-0.165552	-0.230327	-0.354822	0.239717	-0.239717
wchar	0.174410	0.132595	0.331933	0.415059	0.430126	0.122990	0.124145	0.486317	1.000000	0.195926	...	0.160142	0.250258	0.260535	0.125757	0.157922	-0.146838	-0.175309	-0.231823	0.254992	-0.254992
pgout	0.208258	0.090726	0.297487	0.301658	0.274708	0.197047	0.230712	0.250881	0.195926	1.000000	...	0.642940	0.437916	0.448769	0.206128	0.299740	-0.469831	-0.348040	-0.258025	0.020791	-0.020791
ppgout	0.219798	0.089149	0.305507	0.315648	0.284982	0.214192	0.248680	0.267983	0.204542	0.950418	...	0.614961	0.464484	0.482710	0.221593	0.320826	-0.461413	-0.338905	-0.253616	0.031776	-0.031776
pgfree	0.214400	0.082519	0.300612	0.311863	0.280082	0.216166	0.250468	0.261728	0.187786	0.909151	...	0.598164	0.464531	0.483354	0.221590	0.322354	-0.464708	-0.339652	-0.249313	0.035134	-0.035134
pgscan	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
atch	0.230628	0.126769	0.306359	0.292044	0.264283	0.201768	0.252416	0.265281	0.160142	0.642940	...	1.000000	0.329873	0.334002	0.208409	0.296459	-0.442062	-0.345789	-0.232467	0.044710	-0.044710
pgin	0.283252	0.113368	0.335380	0.346654	0.307420	0.247715	0.299591	0.369470	0.250258	0.437916	...	0.329873	1.000000	0.961242	0.252192	0.396702	-0.309647	-0.365383	-0.292180	0.094704	-0.094704
ppgin	0.290564	0.118388	0.325487	0.344586	0.302481	0.236216	0.288017	0.390376	0.260535	0.448769	...	0.334002	0.961242	1.000000	0.244019	0.384168	-0.301705	-0.348716	-0.290894	0.095999	-0.095999
pflt	0.375250	0.102631	0.485361	0.529364	0.505560	0.939125	0.758255	0.381489	0.125757	0.206128	...	0.208409	0.252192	0.244019	1.000000	0.931879	-0.132769	-0.132720	-0.366780	0.124093	-0.124093
vflt	0.421079	0.135243	0.548081	0.597892	0.563163	0.932579	0.763429	0.438870	0.157922	0.299740	...	0.296459	0.396702	0.384168	0.931879	1.000000	-0.228413	-0.258084	-0.416698	0.136130	-0.136130
freemem	-0.201369	-0.099558	-0.388969	-0.349887	-0.350318	-0.136045	-0.191919	-0.165552	-0.146838	-0.469831	...	-0.442062	-0.309647	-0.301705	-0.132769	-0.228413	1.000000	0.607000	0.299864	-0.118178	0.118178
freeswap	-0.243903	-0.149630	-0.357864	-0.369897	-0.336869	-0.133135	-0.184268	-0.230327	-0.175309	-0.348040	...	-0.345789	-0.365383	-0.348716	-0.132720	-0.258084	0.607000	1.000000	0.676250	-0.065494	0.065494
usr	-0.292817	-0.148599	-0.325174	-0.388342	-0.350115	-0.351232	-0.325499	-0.354822	-0.231823	-0.258025	...	-0.232467	-0.292180	-0.290894	-0.366780	-0.416698	0.299864	0.676250	1.000000	-0.261980	0.261980
CPU_Bound_CPU_Bound	0.136419	0.053363	0.247893	0.228820	0.216083	0.122392	0.125470	0.239717	0.254992	0.020791	...	0.044710	0.094704	0.095999	0.124093	0.136130	-0.118178	-0.065494	-0.261980	1.000000	-1.000000
CPU_Bound_Not_CPU_Bound	-0.136419	-0.053363	-0.247893	-0.228820	-0.216083	-0.122392	-0.125470	-0.239717	-0.254992	-0.020791	...	-0.044710	-0.094704	-0.095999	-0.124093	-0.136130	0.118178	0.065494	0.261980	-1.000000	1.000000

	lread	lwrite	scall	sread	swrite	fork	exec	rchar	wchar	pgout	...	atch	pgin	ppgin	pflt	vflt	freemem	freeswap	usr	CPU_Bound_CPU_Bound	CPU_Bound_Not_CPU_Bound
lread	2.298178e+02	117.575085	8.056049e+03	7.253641e+02	4.514278e+02	8.821001	11.194106	6.763548e+05	1.884203e+05	6.946479	...	1.968174	3.299712e+01	4.916244e+01	5.776797e+02	1.037292e+03	-4.901926e+03	-1.555845e+06	-8.168663e+01	1.032398	-1.032398
lwrite	1.175751e+02	86.340235	2.075570e+03	2.018620e+02	1.192355e+02	1.377515	2.295088	1.906240e+05	8.780034e+04	1.854865	...	0.663102	8.094828e+00	1.227759e+01	9.684088e+01	2.042052e+02	-1.485465e+03	-5.850364e+05	-2.540878e+01	0.247528	-0.247528
scall	8.056049e+03	2075.569565	2.537947e+06	1.783903e+05	1.148608e+05	1202.815418	1421.760240	1.076042e+08	3.768382e+07	1042.752394	...	274.746170	4.105718e+03	5.787281e+03	7.852013e+04	1.418834e+05	-9.950339e+05	-2.398928e+08	-9.532783e+03	197.144826	-197.144826
sread	7.253641e+02	201.861981	1.783903e+05	2.153818e+04	1.249793e+04	123.312458	110.053378	1.476182e+07	4.340885e+06	97.407481	...	24.127527	3.909416e+02	5.644194e+02	7.889214e+03	1.425845e+04	-8.245453e+04	-2.284248e+07	-1.048773e+03	16.764064	-16.764064
swrite	4.514278e+02	119.235470	1.148608e+05	1.249793e+04	9.436536e+03	80.246792	61.733552	7.119080e+06	2.977596e+06	58.715107	...	14.452229	2.294826e+02	3.279483e+02	4.987170e+03	8.889667e+03	-5.464495e+04	-1.376972e+07	-6.258640e+02	10.478689	-10.478689
fork	8.821001e+00	1.377515	1.202815e+03	1.233125e+02	8.024679e+01	2.531981	2.498871	1.030661e+05	1.394643e+04	0.689878	...	0.180735	3.028963e+00	4.195076e+00	1.517499e+02	2.411355e+02	-3.476124e+02	-8.914164e+04	-1.028458e+01	0.097222	-0.097222
exec	1.119411e+01	2.295088	1.421760e+03	1.100534e+02	6.173355e+01	2.498871	4.113812	1.150827e+05	1.794388e+04	1.029592	...	0.288203	4.669408e+00	6.519885e+00	1.561753e+02	2.516149e+02	-6.250611e+02	-1.572640e+05	-1.214883e+01	0.127040	-0.127040
rchar	6.763548e+05	190624.001809	1.076042e+08	1.476182e+07	7.119080e+06	103066.059847	115082.723440	3.048139e+10	6.050629e+09	96373.614414	...	26072.565937	4.956877e+05	7.606787e+05	6.763551e+06	1.245085e+07	-4.641242e+07	-1.692079e+10	-1.139964e+06	20892.770729	-20892.770729
wchar	1.884203e+05	87800.339956	3.768382e+07	4.340885e+06	2.977596e+06	13946.433429	17943.883423	6.050629e+09	5.078409e+09	30720.472730	...	6424.325284	1.370452e+05	2.072193e+05	9.100651e+05	1.828735e+06	-1.680288e+07	-5.256850e+09	-3.040070e+05	9071.330124	-9071.330124
pgout	6.946479e+00	1.854865	1.042752e+03	9.740748e+01	5.871511e+01	0.689878	1.029592	9.637361e+04	3.072047e+04	4.841105	...	0.796347	7.404139e+00	1.102036e+01	4.605586e+01	1.071674e+02	-1.659952e+03	-3.222249e+05	-1.044712e+01	0.022836	-0.022836
ppgout	1.345266e+01	3.344377	1.964967e+03	1.870254e+02	1.117677e+02	1.376026	2.036364	1.888938e+05	5.884906e+04	8.442669	...	1.397656	1.441036e+01	2.175101e+01	9.084967e+01	2.104789e+02	-2.991328e+03	-5.757434e+05	-1.884227e+01	0.064043	-0.064043
pgfree	1.619710e+01	3.821042	2.386539e+03	2.280811e+02	1.355854e+02	1.714109	2.531604	2.277134e+05	6.668815e+04	9.968485	...	1.678033	1.778880e+01	2.688356e+01	1.121364e+02	2.610357e+02	-3.718630e+03	-7.122181e+05	-2.286280e+01	0.087402	-0.087402
pgscan	0.000000e+00	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	0.000000	0.000000e+00	0.000000e+00	0.000000	...	0.000000	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000	0.000000
atch	1.968174e+00	0.663102	2.747462e+02	2.412753e+01	1.445223e+01	0.180735	0.288203	2.607257e+04	6.424325e+03	0.796347	...	0.316898	1.426978e+00	2.098500e+00	1.191381e+01	2.711873e+01	-3.995992e+02	-8.190831e+04	-2.408149e+00	0.012564	-0.012564
pgin	3.299712e+01	8.094828	4.105718e+03	3.909416e+02	2.294826e+02	3.028963	4.669408	4.956877e+05	1.370452e+05	7.404139	...	1.426978	5.905030e+01	8.244116e+01	1.967966e+02	4.953594e+02	-3.820845e+03	-1.181456e+06	-4.131652e+01	0.363294	-0.363294
ppgin	4.916244e+01	12.277592	5.787281e+03	5.644194e+02	3.279483e+02	4.195076	6.519885	7.606787e+05	2.072193e+05	11.020360	...	2.098500	8.244116e+01	1.245663e+02	2.765654e+02	6.967340e+02	-5.407107e+03	-1.637685e+06	-5.974444e+01	0.534870	-0.534870
pflt	5.776797e+02	96.840877	7.852013e+04	7.889214e+03	4.987170e+03	151.749909	156.175312	6.763551e+06	9.100651e+05	46.055858	...	11.913810	1.967966e+02	2.765654e+02	1.031216e+04	1.537729e+04	-2.164983e+04	-5.671123e+06	-6.853979e+02	6.290760	-6.290760
vflt	1.037292e+03	204.205243	1.418834e+05	1.425845e+04	8.889667e+03	241.135532	251.614928	1.245085e+07	1.828735e+06	107.167425	...	27.118733	4.953594e+02	6.967340e+02	1.537729e+04	2.640529e+04	-5.960016e+04	-1.764672e+07	-1.246033e+03	11.042795	-11.042795
freemem	-4.901926e+03	-1485.465200	-9.950339e+05	-8.245453e+04	-5.464495e+04	-347.612351	-625.061085	-4.641242e+07	-1.680288e+07	-1659.952198	...	-399.599217	-3.820845e+03	-5.407107e+03	-2.164983e+04	-5.960016e+04	2.578476e+06	4.101365e+08	8.860726e+03	-94.732777	94.732777
freeswap	-1.555845e+06	-585036.378114	-2.398928e+08	-2.284248e+07	-1.376972e+07	-89141.643479	-157264.039507	-1.692079e+10	-5.256850e+09	-322224.867945	...	-81908.311619	-1.181456e+06	-1.637685e+06	-5.671123e+06	-1.764672e+07	4.101365e+08	1.770581e+11	5.236344e+06	-13757.489316	13757.489316
usr	-8.168663e+01	-25.408783	-9.532783e+03	-1.048773e+03	-6.258640e+02	-10.284579	-12.148834	-1.139964e+06	-3.040070e+05	-10.447125	...	-2.408149	-4.131652e+01	-5.974444e+01	-6.853979e+02	-1.246033e+03	8.860726e+03	5.236344e+06	3.386301e+02	-2.406643	2.406643
CPU_Bound_CPU_Bound	1.032398e+00	0.247528	1.971448e+02	1.676406e+01	1.047869e+01	0.097222	0.127040	2.089277e+04	9.071330e+03	0.022836	...	0.012564	3.632939e-01	5.348703e-01	6.290760e+00	1.104280e+01	-9.473278e+01	-1.375749e+04	-2.406643e+00	0.249208	-0.249208
CPU_Bound_Not_CPU_Bound	-1.032398e+00	-0.247528	-1.971448e+02	-1.676406e+01	-1.047869e+01	-0.097222	-0.127040	-2.089277e+04	-9.071330e+03	-0.022836	...	-0.012564	-3.632939e-01	-5.348703e-01	-6.290760e+00	-1.104280e+01	9.473278e+01	1.375749e+04	2.406643e+00	-0.249208	0.249208

	lwrite	scall	exec	rchar	wchar	pgscan	atch	freemem	freeswap
0	0.0	2147.0	0.20	40671.0	53995.0	0.0	0.0	4659.125	1730946.0
1	0.0	170.0	0.20	448.0	8385.0	0.0	0.0	4659.125	1869002.0
2	3.0	2162.0	2.40	125473.5	31950.0	0.0	1.2	702.000	1021237.0
3	0.0	160.0	0.20	125473.5	8670.0	0.0	0.0	4659.125	1863704.0
4	1.0	330.0	0.40	125473.5	12185.0	0.0	0.0	633.000	1760253.0
...	...	...	...	...	...	...	...	...	...
8187	12.0	3009.0	5.81	405250.0	85282.0	0.0	0.6	387.000	986647.0
8188	0.0	1596.0	1.80	89489.0	41764.0	0.0	0.8	263.000	1055742.0
8189	5.0	3116.0	0.60	325948.0	52640.0	0.0	0.4	400.000	969106.0
8190	25.0	5180.0	1.20	62571.0	29505.0	0.0	0.4	141.000	1022458.0
8191	0.0	985.0	4.80	111111.0	22256.0	0.0	0.2	659.000	1756514.0

LOAD AND DESCRIBE THE DATA¶

PERFORM EDA AND DATA CLEANING¶

Build a linear Regression Model(scaled)¶

	value	Missing?
0	40671.0	False
1	448.0	False
2	NaN	True
3	NaN	True
4	NaN	True
...	...	...
8187	405250.0	False
8188	89489.0	False
8189	325948.0	False
8190	62571.0	False
8191	111111.0	False

	lread	lwrite	scall	sread	swrite	fork	exec	rchar	wchar	pgout	...	pgscan	atch	pgin	ppgin	pflt	vflt	runqsz	freemem	freeswap	usr
0	1	0	2147	79	68	0.2	0.20	40671.0	53995.0	0.00	...	0.00	0.0	1.60	2.60	16.00	26.40	CPU_Bound	4670	1730946	95
1	0	0	170	18	21	0.2	0.20	448.0	8385.0	0.00	...	0.00	0.0	0.00	0.00	15.63	16.83	Not_CPU_Bound	7278	1869002	97
2	15	3	2162	159	119	2.0	2.40	125473.5	31950.0	0.00	...	0.00	1.2	6.00	9.40	150.20	220.20	Not_CPU_Bound	702	1021237	87
3	0	0	160	12	16	0.2	0.20	125473.5	8670.0	0.00	...	0.00	0.0	0.20	0.20	15.60	16.80	Not_CPU_Bound	7248	1863704	98
4	5	1	330	39	38	0.4	0.40	125473.5	12185.0	0.00	...	0.00	0.0	1.00	1.20	37.80	47.60	Not_CPU_Bound	633	1760253	90
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
8187	16	12	3009	360	244	1.6	5.81	405250.0	85282.0	8.02	...	55.11	0.6	35.87	47.90	139.28	270.74	CPU_Bound	387	986647	80
8188	4	0	1596	170	146	2.4	1.80	89489.0	41764.0	3.80	...	0.20	0.8	3.80	4.40	122.40	212.60	Not_CPU_Bound	263	1055742	90
8189	16	5	3116	289	190	0.6	0.60	325948.0	52640.0	0.40	...	0.00	0.4	28.40	45.20	60.20	219.80	Not_CPU_Bound	400	969106	87
8190	32	45	5180	254	179	1.2	1.20	62571.0	29505.0	1.40	...	18.04	0.4	23.05	24.25	93.19	202.81	CPU_Bound	141	1022458	83
8191	2	0	985	55	46	1.6	4.80	111111.0	22256.0	0.00	...	0.00	0.2	3.40	6.20	91.80	110.00	CPU_Bound	659	1756514	94

	lread	lwrite	scall	sread	swrite	fork	exec	rchar	wchar	...	atch	pgin	ppgin	pflt	vflt	freemem	freeswap	usr	CPU_Bound_CPU_Bound	CPU_Bound_Not_CPU_Bound
0	1.0	0.0	2147.0	79.0	68.0	0.2	0.2	40671.0	53995.0	...	0.0	1.6	2.6	16.00	26.40	4659.125	1730946.0	95	1	0
1	0.0	0.0	170.0	18.0	21.0	0.2	0.2	448.0	8385.0	...	0.0	0.0	0.0	15.63	16.83	4659.125	1869002.0	97	0	1
2	15.0	3.0	2162.0	159.0	119.0	2.0	2.4	125473.5	31950.0	...	1.2	6.0	9.4	150.20	220.20	702.000	1021237.0	87	0	1
3	0.0	0.0	160.0	12.0	16.0	0.2	0.2	125473.5	8670.0	...	0.0	0.2	0.2	15.60	16.80	4659.125	1863704.0	98	0	1
4	5.0	1.0	330.0	39.0	38.0	0.4	0.4	125473.5	12185.0	...	0.0	1.0	1.2	37.80	47.60	633.000	1760253.0	90	0	1

	lwrite	scall	exec	rchar	wchar	atch	freemem	freeswap	usr
5745	10.0	2007.0	0.60	32665.0	49643.0	0.6	329.0	989029.0	94
1930	4.0	837.0	0.80	6255.0	23670.0	0.0	3052.0	1013758.0	96
5622	2.0	2227.0	6.70	108370.0	28568.0	0.2	314.0	1108418.0	77
5340	0.0	2132.0	0.20	90813.0	28590.0	0.0	488.0	1742493.0	93
2255	0.0	3517.0	2.99	310439.0	214462.0	0.2	420.0	1547657.0	86